World Wide Technology
End to End Architect
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End to End Architect
World Wide Technology
London - Onsite 2 days per week (Must be UK based)
UAE - Relocation and sponsorship
Full Time - Permanent
Most AI infrastructure failures happen at the seams, where compute meets network, where storage meets platform, where facilities meet hardware. You will be the integrator who prevents this.
As an End to End Architect, you bring together compute, networking, storage, facilities, platform, and security into coherent architectures that actually work when thousands of GPUs need to train a model together. You will be the named Design Authority on WWT’s most complex AI infrastructure programmes, ensuring every component works as a system.
What You’ll Do
- Integrate compute, networking, storage, facilities, and platform layers into unified AI infrastructure architectures
- Serve as named Design Authority on major AI infrastructure programmes, owning the architectural integrity end-to-end
- Map AI infrastructure architectures to client business outcomes: cost per training run, time to inference, utilisation efficiency
- Evaluate and recommend vendor neutral component stacks: GPU compute (NVIDIA, AMD), networking, storage, and platform
- Design reference architectures for common AI infrastructure patterns: training clusters, inference farms, hybrid training/inference
- Coordinate between domain specialists (networking, storage, MEP, platform) to resolve cross domain design conflicts
- Conduct architectural reviews and risk assessments at programme milestones
- Present architecture decisions and trade-offs to client CTO/VP-level stakeholders
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Must Haves
- Extensive experience in infrastructure and solution architecture, spanning compute, networking, and storage
- 3/5+ years designing AI/ML or HPC infrastructure platforms at scale
- TOGAF, SABSA, or equivalent architecture framework experience
- Vendor neutral evaluation capability across GPU compute, networking, storage, and platform vendors
- Client-facing design authority experience on programmes of significant scale
- Systems level thinking and the ability to understand how component interactions affect overall system performance
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Nice to Haves
- Experience with NVIDIA DGX/HGX SuperPOD reference architectures
- Understanding of AI platform software: Kubernetes, Slurm, Base Command Manager
- Knowledge of TCO modelling for AI infrastructure (build vs colo vs cloud)
- Experience with air-gapped or sovereign AI infrastructure deployments
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